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THE INFLUENCE OF NETWORK STRUCTURE ON THE PERFORMANCE OF LEARNING SYSTEMS,
BELL AEROSPACE CORP BUFFALO N Y
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The report summarizes research on means for improving the discrimination capability of linear single-gain layer networks. Improvements are considered via a nonlinear pre-processors, such as the AD converter, the quantizer, and other encoders, the generation of functionals of the individual pattern components, and b cascading of these networks. All result essentially in introducing a nonlinear decision surface instead of the decision hyperplane as defined by a linear net. Significant improvements are obtainable that widen the application of such networks to practical problems. As an example, the modeling of an air cushion vehicle is discussed. Author
APPROVED FOR PUBLIC RELEASE